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Particles interacting (causally) though a physical mechanism that gives rise to say "wholes" with novel causal properties is not a statistical process. So your

Particles interacting (causally) though a physical mechanism that gives rise to say "wholes" with novel causal properties is not a statistical process. So your premise contradicts your conclusion. Statistics is an analysis of association, not of causation. The frequency of (Q, A) pairs follows a distribution that is not constrained, or caused by, or explained by, how Q and A are actually related. For example, recently there was some scandal at microsoft that if you used "pro choice" in prompts you got "demonic cartoons". Why? Presumably because "pro choice" are symbols that accompany such political cartoons in the data set. So does Q = "pro choice", and A = "cartoons of hell" occur at notable frequency because hell has caused anything? Or because there's a unique semantic mechanism where by "pro choice" means "hell" and so on. NO. It is absolutely insane to suggest that we have rigged all our text output so as to align one set of symbols (Q) alongside another (A) such that, Q is the necessary explanation of A. I doubt this is even possible, since most Qs dont have unique As -- so there is actually *no function* to approximate. In any case, your whole comment is an argument from ignorance as I complained in mine. What you don't know about life, about machines, about intelligence justifies no conclusions at all (esp., "everything in life could be"). And let's be clear. Lots of people do know the answers to your questions, they arent hard to answer. It's just not in any ad companies interest to lead their description of these systems by presenting good-faith research. Everything printed in the media today is just a game of stock manipulation using the "prognosticator loopwhole" whereby the CEO of nvidia can "prophesy the future" in which his hardware is "of course" essential -- without being held to account for his statements. So when that stock hits its ATH and crashes, no one can sue. I think we should change this; remove this loophole and suddenly tech boards and propagandists will be much much more reserved.

mewpmewp2 3y ago
What could be "statistics" is our intelligence learning from past events, either by natural selection in the scope of generations or our brains during our lifetime. If certain A outcome has occured enough times for Q input, it has resulted in such a structure that is best given the resources available to reach that.
- mjburgess 3y ago
  Suppose you touch a fireplace once, do you touch it again? No. OK, here's something much stranger. Suppose you see your friend touch the fireplace, he recoils in pain. Do you touch it? No. Hmm... whence statistics? There is no frequency association here, in either case. And in the second, even no experience of the fireplace. The entire history of science is supposed to be about the failure of statistics to produce explanations. It is a great sin that we have allowed pseudosciences to flourish in which this lesson isnt even understood; and worse, to allow statistical showmen with their magic lanterns to preach on the scientific method. To a point where it seems, almost, science as an ideal has been completely lost. The entire point was to throw away entirely our reliance on frequency and association -- this is ancient superstition. And instead, to explain the world by necessary mechanisms born of causal properties which interact in complex ways that can never uniquely reveal themselves by direct measurement.
  - deleted 3y ago
    [deleted]
  - calf 3y ago
    I get the Chomskyan objection re. statistical machine learning, I am partial to it. But consider these LLMs and such as extremely crude simulations of biological neural networks. They aren't just any statistics; these are biomimetic computations. Then we can in principle "do science" here. We can study skyscrapers and bridges; we can study LLMs and say some scientific things about them. That is quite different than maybe what is going on in industry, but AFAIK there are lots of academic computer scientists who are trying to do just that, bring the science back to the study of these artifacts so that we can have some theorems and proofs, etc. That is - hopefully - more sophisticated a science than trying to poke and prod at a black box and call that empirical science.
    - tsimionescu 3y ago
      The only relationship between artificial neural networks and biology is the vague principle of an activation threshold. In all other ways, the way biological neural networks are organized are ignored. The majority of ANN characteristics are instead influenced by statistics and mathematical arguments, and simple tinkering. For some ways in which these differ: - real neurons are vastly complex cells which seem to perform significant non-trivial computations of their own, including memory, and can't be abstracted as a single activation function - real neural networks include "broadcast neurons" that affect other neurons based on their geometric organization, not on direct synapse connections - there are various neurotransmitters in the brain with different effects, not just a single type of "signal" - several glands affect thought and computation in the brain that are not made of neurons - real neural networks are not organized in a simple set of layers, they form much more complex graphs And these are just talking about the structure. The way we actually operate these networks has nothing in common with how real neural networks work. Real neural networks have no separate training and inference phases: at all times, they both learn and produce actionable results. Also, the way in which we train ANNs, backpropagation with stochastic gradient descent, is entirely unrelated from how real neural networks learn (which we don't really understand in any significant amount). So no, I don't think it makes any sense to say that ANNs are a form of biomimetic computation. They are at best as similar to brains as a nylon coat is to an animal's coat.

mewpmewp2 3y ago

What could be "statistics" is our intelligence learning from past events, either by natural selection in the scope of generations or our brains during our lifetime. If certain A outcome has occured enough times for Q input, it has resulted in such a structure that is best given the resources available to reach that.

mjburgess 3y ago
Suppose you touch a fireplace once, do you touch it again? No. OK, here's something much stranger. Suppose you see your friend touch the fireplace, he recoils in pain. Do you touch it? No. Hmm... whence statistics? There is no frequency association here, in either case. And in the second, even no experience of the fireplace. The entire history of science is supposed to be about the failure of statistics to produce explanations. It is a great sin that we have allowed pseudosciences to flourish in which this lesson isnt even understood; and worse, to allow statistical showmen with their magic lanterns to preach on the scientific method. To a point where it seems, almost, science as an ideal has been completely lost. The entire point was to throw away entirely our reliance on frequency and association -- this is ancient superstition. And instead, to explain the world by necessary mechanisms born of causal properties which interact in complex ways that can never uniquely reveal themselves by direct measurement.
- deleted 3y ago
  [deleted]
- calf 3y ago
  I get the Chomskyan objection re. statistical machine learning, I am partial to it. But consider these LLMs and such as extremely crude simulations of biological neural networks. They aren't just any statistics; these are biomimetic computations. Then we can in principle "do science" here. We can study skyscrapers and bridges; we can study LLMs and say some scientific things about them. That is quite different than maybe what is going on in industry, but AFAIK there are lots of academic computer scientists who are trying to do just that, bring the science back to the study of these artifacts so that we can have some theorems and proofs, etc. That is - hopefully - more sophisticated a science than trying to poke and prod at a black box and call that empirical science.
  - tsimionescu 3y ago
    The only relationship between artificial neural networks and biology is the vague principle of an activation threshold. In all other ways, the way biological neural networks are organized are ignored. The majority of ANN characteristics are instead influenced by statistics and mathematical arguments, and simple tinkering. For some ways in which these differ: - real neurons are vastly complex cells which seem to perform significant non-trivial computations of their own, including memory, and can't be abstracted as a single activation function - real neural networks include "broadcast neurons" that affect other neurons based on their geometric organization, not on direct synapse connections - there are various neurotransmitters in the brain with different effects, not just a single type of "signal" - several glands affect thought and computation in the brain that are not made of neurons - real neural networks are not organized in a simple set of layers, they form much more complex graphs And these are just talking about the structure. The way we actually operate these networks has nothing in common with how real neural networks work. Real neural networks have no separate training and inference phases: at all times, they both learn and produce actionable results. Also, the way in which we train ANNs, backpropagation with stochastic gradient descent, is entirely unrelated from how real neural networks learn (which we don't really understand in any significant amount). So no, I don't think it makes any sense to say that ANNs are a form of biomimetic computation. They are at best as similar to brains as a nylon coat is to an animal's coat.